The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →You do not need an AI HAT to run local AI on a Raspberry Pi 5. The Pi can provide a CPU-only baseline, but Raspberry Pi’s documented Hailo accelerators are intended to run supported neural-network workloads more efficiently than the CPU. For camera vision, object detection, segmentation, pose estimation, robotics, and similar models, choose an AI HAT+ tier according to the model and throughput you need. For local large-language or vision-language models, the documented fit is the AI HAT+ 2.
Do not read 13, 26, or 40 TOPS as a guaranteed speed multiplier over the CPU. Those are accelerator specifications measured at different precisions, not a controlled Pi 5 comparison using the same model, runtime, quantization, cooling, and workload.
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CPU inference and an AI HAT solve different problems
“Local AI on Raspberry Pi 5” can mean two distinct arrangements:
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- Accelerated inference: a supported model is compiled or configured for a Hailo neural-processing unit (NPU) on an AI HAT, while the Pi handles application logic, input/output, and orchestration.
Raspberry Pi describes the NPU this way: “The Hailo NPU is an AI accelerator chip designed to run neural networks; instead of Raspberry Pi device’s CPU doing the AI work, the NPU handles it more efficiently.” That is a description of the intended division of labor, not a published numerical benchmark. The official material does not establish a universal CPU-versus-HAT latency or frames-per-second ratio.
#1 Best Overall
- Hailo-10H AI accelerator delivering 40 TOPS (INT4) inferencing performance.
- Performance for computer vision models comparable to the Raspbery Pi AI HAT+ (26 TOPS).
- Runs generative AI models efficiently using 8GB on-board RAM.
- Fully integrated into Raspbery Pi’s camera software stack.
- Conforms to Raspbery Pi HAT+ specification.
As a result, the first question is not “How many times faster is the HAT?” It is “Does my model have a supported Hailo path, and what latency, throughput, memory, and concurrency does my project require?”
At-a-glance comparison
| Option | Compute path | Documented workload fit | Published accelerator figure | Memory arrangement | Important boundary |
|---|---|---|---|---|---|
| Pi 5 CPU only | Host CPU and Raspberry Pi 5 memory | Depends on the model and runtime you select | No like-for-like CPU figure is provided in the official material reviewed | Uses host system memory | Speed, model size, and thermals depend on the implementation; no sourced comparison number is available |
| AI HAT+ with Hailo-8L | Hailo NPU | Supported vision and neural-network workloads | 13 TOPS, INT8 | Uses Raspberry Pi 5 memory | Raspberry Pi does not list local LLM support for this product |
| AI HAT+ with Hailo-8 | Hailo NPU | Supported vision and neural-network workloads | 26 TOPS, INT8 | Uses Raspberry Pi 5 memory | Still requires a supported model and software stack |
| AI HAT+ 2 with Hailo-10H | Hailo NPU plus onboard memory | AI HAT+ vision workloads, local LLMs, and vision-language models | 40 TOPS, INT4 | 8 GB onboard memory in addition to host memory | Raspberry Pi describes support for models up to approximately 6 billion parameters; actual compatibility and speed depend on software and model support |
The AI HAT+ figures and workload descriptions are from Raspberry Pi’s AI HAT documentation. The figures use different precisions: AI HAT+ is specified at INT8, while AI HAT+ 2 is specified at INT4. They should not be compared as application performance numbers.
When CPU-only inference is the sensible choice
You are proving the application, not optimizing throughput
A CPU baseline is useful when you are validating camera capture, preprocessing, post-processing, user interfaces, control loops, or a model that you may later replace. It avoids buying hardware before you know that the project’s inputs and outputs are correct. Measure the complete application, not only model execution: image decoding, resizing, communication, and actuation can dominate a small workload.
Your model has no supported Hailo implementation
An AI HAT does not automatically accelerate every neural-network file. Raspberry Pi says automatic detection and offloading apply to supported AI workloads, and that the required software components and supported models are still needed. A model that runs on a CPU runtime may require conversion, a different quantization, or a different architecture before it can use the NPU. If that path does not exist for your model, the HAT may add cost and complexity without moving the workload.
The workload is intermittent
For an occasional classification or a low-duty-cycle sensor check, CPU execution can be adequate even if an accelerator would reduce per-inference time. Consider end-to-end responsiveness and energy draw rather than a headline TOPS value. A project that sleeps between requests has a different requirement from a continuously running multi-camera system.
What CPU-only does not prove
A CPU prototype does not tell you how the same model will perform on an NPU. Different runtimes, quantization, operators, and preprocessing paths can change the result. Keep the CPU version as a correctness reference, then test the supported Hailo version separately if you need a production decision.
What the standard AI HAT+ is for
Vision and neural-network pipelines
Raspberry Pi positions the AI HAT+ for supported tasks including object detection, image segmentation, pose estimation, camera post-processing, and robotics. These are workloads in which a fixed, continuously repeated neural-network pipeline can benefit from a dedicated inference engine while the Pi runs the surrounding application.
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Two performance tiers
The Hailo-8L version is specified at 13 TOPS (INT8). The Hailo-8 version is specified at 26 TOPS (INT8). Treat those as product-tier specifications, not as guaranteed frames per second. Your result depends on the model graph, input resolution, quantization, post-processing, camera rate, and the number of streams.
Why it is not the documented LLM choice
Raspberry Pi’s AI HAT documentation explicitly says LLMs are not supported on AI HAT+. If your primary requirement is a local text-generation model, buying the 13-TOPS or 26-TOPS board on the assumption that it is an LLM accelerator is the wrong match. Use a CPU prototype only to test a small or experimental model, or evaluate AI HAT+ 2 for the documented generative-AI path.
What AI HAT+ 2 adds for local generative AI
Dedicated memory and a different accelerator
AI HAT+ 2 uses a Hailo-10H specified at 40 TOPS (INT4) and includes 8 GB of onboard memory. That memory is separate from the Raspberry Pi 5’s host memory, which is significant for generative workloads that otherwise compete with the operating system and application.
Rank #2
- HIGH PERFORMANCE: Features 26 TOPS (Trillion Operations Per Second) AI acceleration capability through the Hailo AI Accelerator for advanced machine learning applications
- COMPATIBILITY: Specifically designed for the Raspberry Pi 5, connecting via PCIe interface for optimal data transfer and processing speeds
- COMPACT DESIGN: Measures 65mm x 56.5mm, offering a space-efficient solution while maintaining full functionality as an AI acceleration add-on board
- TEMPERATURE RANGE: Operates reliably in temperatures from 0°C to +50°C (32°F to 122°F), ensuring stable performance in various environments
- SEAMLESS INTEGRATION: Functions as a HAT (Hardware Attached on Top) add-on board, providing plug-and-play compatibility with Raspberry Pi ecosystem
Documented LLM and VLM support
Raspberry Pi documents AI HAT+ 2 for local large-language-model (LLM) and vision-language-model (VLM) workloads, with models up to approximately 6 billion parameters. “Up to” is a supported scale description, not a promise that every six-billion-parameter model will fit, load, or generate at a useful rate. Check the current Hailo GenAI software stack, model list, operator coverage, quantization, and context-length limits before designing around a particular model.
The product’s generative-AI positioning is also described in Raspberry Pi’s January 15, 2026 announcement. It should not be generalized to the standard AI HAT+.
How to choose by workload
Object detection, segmentation, or pose estimation
Start with the AI HAT+ compatibility list and the exact model variant you intend to deploy. The 13-TOPS board may be suitable for one stream or a modest input rate; the 26-TOPS board gives more accelerator capacity for a heavier graph or more concurrent streams. Neither figure alone tells you whether your target frame rate will be met.
Robotics and control
Separate the inference deadline from the control-loop deadline. A detector that runs every tenth camera frame may be acceptable if the robot’s controller remains responsive. If several cameras or models must run concurrently, model support and aggregate throughput matter more than the nominal TOPS figure.
Camera post-processing
Use a supported camera and the documented Raspberry Pi camera software path. Raspberry Pi names Camera Module 3 as an example of a supported camera for vision AI. The camera is an additional hardware prerequisite; the HAT alone cannot provide image input.
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Choose AI HAT+ 2 as the documented product fit, then verify that your selected LLM or VLM is supported by the current software. A standard AI HAT+ is not the documented LLM option.
Unusual, custom, or rapidly changing models
Keep the CPU baseline until you have confirmed a conversion and deployment path. An accelerator is valuable only when the model can execute on it with the operators, precision, and input shape your application needs.
Hardware, operating-system, and software requirements
- Host: Raspberry Pi 5.
- Operating system: a 64-bit Raspberry Pi OS installation for Raspberry Pi’s documented Hailo setup.
- HAT connection: install the AI HAT+ or AI HAT+ 2 on the Pi 5 as specified by the board documentation, with appropriate mechanical clearance and power arrangements for your enclosure.
- Software: the Raspberry Pi AI software components, Hailo runtime or GenAI stack as applicable, and a model supported by that stack.
- Vision input: a supported camera for camera-based examples; Camera Module 3 is one named example.
- Thermal management: keep the Pi 5 within its normal operating temperature range. Cooling can affect sustained behavior, so include it in any performance test.
Raspberry Pi’s AI software documentation explains the supported setup and software relationship. The operating system can detect the accelerator, but detection is not the same as model compatibility: you still need the relevant components and a supported model.
Installation and validation workflow
- Define the workload. Record the model name, input dimensions, precision or quantization, expected camera rate, number of simultaneous streams, and the maximum acceptable end-to-end latency.
- Decide whether the model is in scope. For AI HAT+, verify that it is a supported vision or neural-network workload. For AI HAT+ 2, verify the model in the current LLM/VLM or GenAI support information. If no supported path is listed, retain the CPU plan.
- Prepare the host. Use a Raspberry Pi 5 with 64-bit Raspberry Pi OS and install the AI software components specified in Raspberry Pi’s documentation. Do not assume that a generic Python package or CPU runtime will offload work automatically.
- Install and connect the HAT. Follow the board’s mechanical and electrical instructions, then boot the Pi and confirm that the operating system detects the accelerator.
- Install the model-specific artifacts. Use the supported model format, compiled network, runtime, and post-processing components required by the Hailo path. Keep the model’s input preprocessing identical to the CPU reference wherever possible.
- Validate one inference. Feed a known image, prompt, or multimodal sample and compare the output with the CPU reference. Check labels, confidence thresholds, token output, image handling, and error behavior before measuring speed.
- Measure the complete pipeline. Record warm-up time, steady-state latency, throughput, CPU utilization, memory use, temperature, and dropped frames or failed requests. Run enough iterations to expose thermal or memory effects.
- Test the real concurrency. Add the number of cameras, prompts, or simultaneous requests the product must handle. A single-stream result cannot establish multi-stream capacity.
Understanding TOPS without overclaiming performance
TOPS means tera operations per second, but the operation definition and numeric precision matter. AI HAT+ publishes 13 TOPS and 26 TOPS at INT8; AI HAT+ 2 publishes 40 TOPS at INT4. Lower-precision arithmetic can produce a different TOPS figure without being a direct measure of application quality, latency, or token generation.
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Real performance also depends on whether every layer is supported by the NPU. Unsupported operators may run on the CPU, require graph changes, or prevent compilation. Input resolution, batch size, memory transfers, post-processing, and scheduling can all become bottlenecks. Therefore, “40 TOPS is three times 13 TOPS” is arithmetic, not a valid prediction that one application will be three times faster.
Rank #3
- ⚡ PoE HAT for Raspberry Pi 5 CM5: PoE HAT F is a Power over Ethernet expansion board for Raspberry Pi 5 and CM5, supporting network connection and power input through one Ethernet cable.
- 🔌 802.3af/at PoE+ Support: This PoE+ HAT supports IEEE 802.3af/at network standard and works with compatible PoE power sourcing equipment for compact wired deployment projects.
- 🧊 Active Cooling Fan and Metal Heatsink: The PoE HAT with cooling fan includes a metal heatsink and high-speed active fan, helping improve heat dissipation and operating stability during long-term use.
- 🔋 5V and 12V Output Headers: Onboard 5V and 12V header outputs provide power options for external peripherals, with up to 25W total output under suitable PoE input and cooling conditions.
- 🧩 40-pin GPIO Stackable Header: Standard 40-pin GPIO stackable header fits Raspberry Pi 5 and CM5 expansion, allowing users to connect compatible HATs and custom project interfaces.
Choosing between 13 TOPS and 26 TOPS AI HAT+
Both standard AI HAT+ variants target the same broad class of supported neural workloads. Use these decision checks:
- Model fit first: confirm that the exact network and required operators are supported.
- Stream count: one low-rate camera has a different requirement from several high-rate cameras.
- Input size: a high-resolution detector generally demands more compute and memory bandwidth than a small preview model.
- Latency target: robotics and interactive applications may prioritize worst-case response over peak throughput.
- Headroom: choose the higher tier when you have measured that the lower tier cannot meet the required workload, not merely because its TOPS number is larger.
Costs, availability, and product life
| Product | Price evidence | How to interpret it |
|---|---|---|
| AI HAT+ 13 TOPS | $70 listed in Raspberry Pi product materials accessed October 13, 2026 | Vendor list-price context; verify the current price and region before purchase |
| AI HAT+ 26 TOPS | $110 listed in the product brief accessed October 13, 2026 | Vendor list-price context; retail pricing and availability can differ |
| AI HAT+ 2 | No price was verified in the cited material | Check the current product information rather than inferring a price from the standard HAT |
The AI HAT+ product materials state production through at least January 2030. That is useful for a long-lived design, but it is not a guarantee of local stock or an unchanged price. Raspberry Pi says the older AI Kit is no longer in production and recommends AI HAT+ or AI HAT+ 2 for new designs; see the AI Kit product page.
Common failure modes and fixes
The HAT is detected but inference stays on the CPU
Detection confirms the hardware connection, not that your application selected an accelerator path. Check that the AI software components are installed, the model is on the supported list, and the application is using the Hailo-compatible runtime and compiled artifacts.
The model loads but produces different results
Compare preprocessing, color order, resize and crop rules, normalization, quantization, confidence thresholds, and post-processing. A numerically different output can come from a changed input pipeline rather than a faulty HAT.
A camera example fails before inference
Verify that the camera is a supported model and that the Raspberry Pi camera framework can access it. The documented camera path requires both the accelerator software and a supported camera; Camera Module 3 is one example named by Raspberry Pi.
An LLM will not run on a standard AI HAT+
This is an expected product boundary, not necessarily a wiring problem. Raspberry Pi does not list LLM support for AI HAT+. Use a CPU-only experiment for a model that fits your host, or move the design to AI HAT+ 2 and verify the GenAI support matrix.
Performance falls during a long run
Log temperature, clock behavior, memory pressure, and queue depth while repeating the test. Sustained workloads can expose thermal or scheduling limits that a short warm-up measurement hides. Report the steady-state result, not only the first inference.
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How to produce a trustworthy CPU-versus-HAT measurement
No controlled CPU-versus-HAT benchmark is supplied by the cited Raspberry Pi material. If your project needs a numerical decision, create one under reproducible conditions:
- Use the same Raspberry Pi 5, power supply, cooling, operating-system build, input data, and application code where the runtimes allow.
- Record the exact model version, precision, quantization, input shape, prompt or image set, and context length.
- Measure cold-start load time separately from warm inference.
- Report median and high-percentile latency, throughput, CPU utilization, host memory, accelerator utilization when available, temperature, and errors.
- Test one stream and the intended concurrent workload.
- Repeat after a sustained run so thermal behavior is represented.
- Compare output quality or task accuracy, not only speed. A faster result that changes detections or generated responses may not meet the project requirement.
Publish the configuration with the result. Without those controls, a claim such as “the HAT is 10 times faster” is not portable to another model, precision, software version, or cooling setup.
Practical buying decision
- Choose CPU-only first when you are exploring, the workload is occasional, or the model has no confirmed Hailo route.
- Choose AI HAT+ 13 TOPS when a supported vision model meets your measured throughput and concurrency needs at the lower tier.
- Choose AI HAT+ 26 TOPS when the same supported vision workload needs more accelerator headroom or more concurrent streams, and testing justifies the extra capacity.
- Choose AI HAT+ 2 when local LLM or VLM inference is a core requirement and your selected models fit the documented Hailo GenAI software path.
The reliable method is workload-led selection: establish the model and software path, measure the complete application, then buy the accelerator tier that meets the requirement. The TOPS label is useful for distinguishing product classes, but it is not a substitute for that test.
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